Zheng Y.,Nanjing University of Information Science and Technology |
Zhao T.,Nanjing University of Information Science and Technology |
Che H.,Chinese Academy of Meteorological Sciences |
Liu Y.,Chinese Academy of Meteorological Sciences |
And 5 more authors.
Science of the Total Environment | Year: 2016
Based on a 20-year (1991-2010) simulation of dust aerosol deposition with the global climate model CAM5.1 (Community Atmosphere Model, version 5.1), the spatial and temporal variations of dust aerosol deposition were analyzed using climate statistical methods. The results indicated that the annual amount of global dust aerosol deposition was approximately 1161 ± 31 Mt, with a decreasing trend, and its interannual variation range of 2.70% over 1991-2010. The 20-year average ratio of global dust dry to wet depositions was 1.12, with interannual variation of 2.24%, showing the quantity of dry deposition of dust aerosol was greater than dust wet deposition. High dry deposition was centered over continental deserts and surrounding regions, while wet deposition was a dominant deposition process over the North Atlantic, North Pacific and northern Indian Ocean. Furthermore, both dry and wet deposition presented a zonal distribution. To examine the regional changes of dust aerosol deposition on land and sea areas, we chose the North Atlantic, Eurasia, northern Indian Ocean, North Pacific and Australia to analyze the interannual and seasonal variations of dust deposition and dry-to-wet deposition ratio. The deposition amounts of each region showed interannual fluctuations with the largest variation range at around 26.96% in the northern Indian Ocean area, followed by the North Pacific (16.47%), Australia (9.76%), North Atlantic (9.43%) and Eurasia (6.03%). The northern Indian Ocean also had the greatest amplitude of interannual variation in dry-to-wet deposition ratio, at 22.41%, followed by the North Atlantic (9.69%), Australia (6.82%), North Pacific (6.31%) and Eurasia (4.36%). Dust aerosol presented a seasonal cycle, with typically strong deposition in spring and summer and weak deposition in autumn and winter. The dust deposition over the northern Indian Ocean exhibited the greatest seasonal change range at about 118.00%, while the North Atlantic showed the lowest seasonal change at around 30.23%. The northern Indian Ocean had the greatest seasonal variation range of dry-to-wet deposition ratio, at around 74.57%, while Eurasia had the lowest, at around 12.14%. © 2016 Elsevier B.V.
Fu X.,Hubei Meteorological Service Center |
Fan H.,Hubei Lightning Protection Center
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) | Year: 2011
The meteorological condition is very important to the navigation in the Three Gorges Project water-area. But the current lockage scheduling model and algorithm that used in the automatic navigation scheduling system cannot deal with the effect of meteorological condition. Based on the meteorological service system in the Three Gorges Project and the rolling horizon procedure that has been used in the navigation management feasibly, the modification method to the mathematical model under the weather forecasting information has been designed. The method realizes to deal with meteorological condition in the current navigation scheduling framework so as to maintain the system feasible.
Ren Y.-J.,Nanjing University of Information Science and Technology |
Ren Y.-J.,Wuhan Regional Climate Center |
Cui J.-X.,Meteorological Bureau of Hubei Province |
Wan S.-Q.,Wuhan Regional Climate Center |
And 2 more authors.
Advances in Climate Change Research | Year: 2013
In Central China, the obvious climate change has happened along with global warming. Based on the observational analysis, the climate change has significant effects, both positive and negative, in every field within the study area, and with the harmful effects far more prevalent. Under the scenario A1B, it is reported that temperature, precipitation, days of heat waves and extreme precipitation intensity will increase at respective rates of 0.38 C per decade, 12.6 mm per decade, 6.4 d and 47 mm per decade in the 21st century. It is widely believed that these climate changes in the future will result in some apparent impacts on agro-ecosystems, water resources, wetland ecosystem, forest ecosystem, human health, energy sectors and other sensitive fields in Central China. Due to the limited scientific knowledge and researches, there are still some shortages in the climate change assessment methodologies and many uncertainties in the climate prediction results. Therefore, it is urgent and essential to increase the studies of the regional climate change adaptation, extend the research fields, and enhance the studies in the extreme weather and climate events to reduce the uncertainties of the climate change assessments.
Sun P.,Hubei Meteorological Service Center |
Sun P.,Hubei Province Meteorological Energy Technology Development Center |
Chen Z.,Hubei Meteorological Service Center |
Chen Z.,Hubei Province Meteorological Energy Technology Development Center |
And 5 more authors.
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | Year: 2015
Model output statistics (MOS) is a relatively simple and reliable method for forecasting solar radiation. Based on improved conventional MOS method, a improved radiation forecast model was set up to improve the forecasting effect. Considering the weakening effect of atmosphere on radiation, the actual radiation was converted to articulation index to remove astronomical solar radiation impact. Besides, owing to greater impacts of different weather conditions on solar radiation, weather type was classified by using Fisher method before modeling. Meteorological elements of the same weather type, at the same times and in the same season were regarded as a same type. Considering weight changes of solar radiation elements resulted by seasonal and diurnal change characteristics of the solar radiation, the forecast model for different seasons and different times was built. Finally, considering the continuity of the system error, the error of initial model forecast value and the actual value could be regarded as a variable which would be used to build later hours forecast equation. The results show that the analog values of the model can reflect the actual changes in solar radiation and meet the modeling requirement. The mean absolute percentage error (MAPE) of improved MOS model is about 20% less than that of conventional MOS model, significantly improving the forecasting results. August 2012 as the forecast period, the MAPE is 28.33% in forecast period, and the rRMSE is 16.20%. These results show a good prediction skill of the model. © 2015, Science Press. All right reserved.
Xu J.,Hubei Meteorological Service Center |
Chen Z.-H.,Hubei Meteorological Service Center |
Tang J.,Hubei Meteorological Service Center |
Li F.,Hubei Meteorological Service Center
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | Year: 2012
In order to improve the power network operation characteristics of BIPV (Building Integrated Photovoltaic) grid-connected generation system including the high-density and multi-access point, the energy production forecasting demands of the BIPV grid-connected generation system are analyzed from two sides of energy management and regional power grid operation dispatch. And the key technologies of the regional BIPV energy production forecasting are discussed. It is illustrated by means of further analysis that the principle prediction method modeling is difficulty. The development direction for the energy production forecasting modeling of the BIPV grid-connected generation system is artificial intelligent methods such as neural network and support vector machines method. And the precision and spatial scale of the numerical solar radiation forecasting cannot meet the demand of the grid operation dispatch. At last the preliminary design of BIPV energy production forecasting system is put forward which adopts the regional solar radiation forecasting method based on multi-point forecast results.